A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

This notebook teaches the new ReqLLM.evaluate/4 API. It starts with one model call. It then uses a plain GenServer to manage work. It does not use Jido or a Jev.Server package.

Jev is an evaluation model. It takes one text or JSON state and a map of named questions. It returns answers, not chat text. ReqLLM sends these questions to TypeSafe and returns a normal %ReqLLM.Response{}. Our Elixir code then turns the answers into issue labels.

Instead of logging into a remote server long-form, like ssh -i ~/.ssh/angela root@[host] -p22 ... make shortcuts!

ssh remoteserver

...and you're in.

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1. Install python3, download tools: ffmpeg and N_m3u8DL-RE
2. Put kdl.py, ffmpeg and N_m3u8DL-RE into same folder
3. Run python kdl.py it will ask you to input kinescope embed link (e.g. https://kinescope.io/embed/1234534) and referrer link (e.g. https://videos.mysite.org). kinescope forbids to access without a referrer.
4. Wait for a download copletion.

kdl.py can automatically select the best quality of a video or you can choose it in a menu provided by N_m3u8DL-RE.
ffmpeg receives parts of the selected video and an audio and muxes them into output mp4 file.

用于减少 LLM 常见编码错误的行为准则。可与项目特定指令合并使用。

权衡说明： 这些准则倾向于谨慎而非速度。对于琐碎的任务，请自行判断。

不要假设。不要隐藏困惑。呈现权衡。